Trajectory Optimization for FSO Based U-IoT Backhaul Networks

Considering many advancements in aerial platform technology, aerial backhaul networks (ABNs) are expected to play a big role in 6th generation (6G) mobile systems. Considering the significant demand in capacity of Internet of Things (IoT) networks, ABNs using free space optical (FSO) signals are receiving attention as an alternative to using traditional radio frequency (RF) signals. However, FSO based ABNs face the challenge of needing to avoid obstacles (e.g., clouds) in the vertical channel to maintain line-of-sight (LoS) between the ground terminals (GTs) and the unmanned aerial vehicle (UAV) based high-altitude platform stations (HAPSs). Therefore, in this paper, a trajectory optimized FSO based UAV-IoT (U-IoT) backhaul network (TOFU) scheme that minimizes the end-to-end outage probability is proposed. Considering moving clouds and obstacles, the TOFU scheme identifies the changing LoS area and applies gradient descent based on optimized control of the HAP and relay UAVs to minimize the end-to-end outage probability. Simulation experiments were conducted to include various cloud properties and orientations, where the results show that the TOFU scheme can provide significant improvements in the end-to-end outage probability and throughput compared to other benchmarked schemes.

Automated summary

This paper proposes a trajectory optimized FSO based UAV-IoT (U-IoT) backhaul network (TOFU) scheme that minimizes the end-to-end outage probability by optimizing the control of the HAP and relay UAVs. Simulation experiments show that the TOFU scheme can provide significant improvements in the end-to-end outage probability and throughput compared to other benchmarked schemes.